Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...

Background: Two main issues that challenge model building are number of Events Per Variable and multicollinearity among exploratory variables. Our aim is to review statistical methods that tackle these issues with emphasize on penalized Lasso regression model.  The present study aimed to explain problems of traditional regressions due to small sample size and m...

Background & Aims: Tuberculosis is one of the most important diseases of century that involves all the human organs including lungs. Due to the yearly occurrence of 9 millions new cases and 1.5 millions deaths in the world, tuberculosis is in the center of attention. The aim of this study was to indentify factors affecting the time required for sputum smear conversion in patien...

background: breast cancer is the most common cancer after lung cancer and the second cause of death. in this study we compared weibull and lognormal cure models with cox regression on the survival of breast cancer. study design: a cohort study. methods: the current study retrospective cohort study was conducted on 140 patients referred to ali ibn abitaleb hospital, rafsanjan southeastern iran f...

results survival probabilities at different times were determined using the cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the kaplan-meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between cox and kaplan-meier (p < 0.05) and no significant difference between cox and...

The purpose of this paper is to construct confidence intervals for the regression coefficients in high-dimensional Cox proportional hazards models where number covariates may be larger than sample size. Our debiased estimator construction similar those Zhang and (2014) van de Geer et al. (2014), but time-dependent censored risk sets introduce considerable additional challenges. theoretical resu...